Dept. of Electr. and Comput. Eng., California Univ., San Diego, CA.
IEEE Trans Image Process. 1993;2(3):327-40. doi: 10.1109/83.236534.
This work studies the performance of dimensional least mean square (TDLMS) adaptive filters as prewhitening filters for the detection of small objects in image data. The object of interest is assumed to have a very small spatial spread and is obscured by correlated clutter of much larger spatial extent. The correlated clutter is predicted and subtracted from the input signal, leaving components of the spatially small signal in the residual output. The receiver operating characteristics of a detection system augmented by a TDLMS prewhitening filter are plotted using Monte-Carlo techniques. It is shown that such a detector has better operating characteristics than a conventional matched filter in the presence of correlated clutter. For very low signal-to-background ratios, TDLMS-based detection systems show a considerable reduction in the number of false alarms. The output energy in both the residual and prediction channels of such filters is shown to be dependent on the correlation length of the various components in the input signal. False alarm reduction and detection gains obtained by using this detection scheme on thermal infrared sensor data with known object positions is presented.
这项工作研究了维纳最小均方（TDLMS）自适应滤波器作为预白化滤波器在图像数据中小目标检测中的性能。假设感兴趣的目标具有非常小的空间扩展，并被相关的大空间范围的杂波所掩盖。相关的杂波被预测并从输入信号中减去，留下空间小信号的分量在残差输出中。使用蒙特卡罗技术绘制了通过 TDLMS 预白化滤波器增强的检测系统的接收机工作特性。结果表明，与传统的匹配滤波器相比，在存在相关杂波的情况下，这种检测器具有更好的工作特性。对于非常低的信噪比，基于 TDLMS 的检测系统显示出虚假警报数量的显著减少。结果表明，这种滤波器的残差和预测通道中的输出能量取决于输入信号中各个分量的相关长度。还介绍了在具有已知目标位置的热红外传感器数据上使用此检测方案获得的虚假警报减少和检测增益。
